{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "indirect-accordance",
   "metadata": {},
   "source": [
    "This notebook reads in previously downsampled data and does a simple least-squares slip inversion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "affecting-percentage",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "# External libraries\n",
    "import numpy as np\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "import os\n",
    "import requests\n",
    "\n",
    "matplotlib.rcParams['figure.figsize'] = (30,30) # make the plots bigger"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "asian-frame",
   "metadata": {},
   "outputs": [],
   "source": [
    "# CSI routines\n",
    "import csi.planarfault as pf\n",
    "import csi.gps as gr\n",
    "import csi.insar as insar\n",
    "import csi.geodeticplot as geoplt\n",
    "import csi.seismiclocations as sl\n",
    "import csi.imagedownsampling as imagedownsampling\n",
    "import csi.fault3D as flt3D\n",
    "import csi.faultwithvaryingdip as flt\n",
    "import csi.verticalfault as verticalfault\n",
    "#import csi.cosicorrrates as cr\n",
    "import csi.imagecovariance as imcov\n",
    "import csi.multifaultsolve as multifaultsolve\n",
    "import csi.transformation as transformation\n",
    "import csi.faultpostproc as faultpp"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "extraordinary-kenya",
   "metadata": {},
   "source": [
    "You should have already completed the downsampling notebook. These flags control what parts of the slip inversion and plotting is done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "received-spiritual",
   "metadata": {},
   "outputs": [],
   "source": [
    "# if not doSolve then read previous solution\n",
    "doSolve = True\n",
    "check_geometry = True\n",
    "plotSAR = True\n",
    "plotGPS = False\n",
    "plotSlip = True\n",
    "save_iCd = False"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "developed-fence",
   "metadata": {},
   "source": [
    "CSI does most computations in a local Cartesian coordinate system and these projection parameters better be the same as what was used in the downsampling. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "abstract-tanzania",
   "metadata": {},
   "outputs": [],
   "source": [
    "# UTM zone 11 for eastern California\n",
    "utmzone = 11\n",
    "# center for local coordinates--M7.1 epicenter from Zach Ross catalog\n",
    "lon0 = -117.5995\n",
    "lat0 = 35.7678"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "id": "fatal-japan",
   "metadata": {},
   "source": [
    "For this example, we will use an InSAR dataset for the 2019 Ridgecrest earthquake, published online at the Harvard Dataverse:\n",
    "\n",
    "Fielding, Eric Jameson, 2019, \"Replication Data for: Surface deformation related to the 2019 Mw 7.1 and Mw 6.4 Ridgecrest Earthquakes in California from GPS, SAR interferometry, and SAR pixel offsets\", https://doi.org/10.7910/DVN/JL9YMS, Harvard Dataverse, V2\n",
    "\n",
    "The dataset we will work with is an ALOS-2 wide-swath interferogram from descending track 166.\n",
    "\n",
    "We set the output name of the downsampled dataset that we will read in."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "stuffed-capital",
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the downsampled (varres) name root\n",
    "varresA2 = 'A2D166_ifg'  # ALOS-2 Descending track 166"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prime-momentum",
   "metadata": {},
   "source": [
    "We instantiate a new `insar` object and read in the downsampled data with the associated data covariance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "streaming-success",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "---------------------------------\n",
      "Initialize InSAR data set A2D166_ifg\n",
      "Read from file A2D166_ifg into data set A2D166_ifg\n"
     ]
    }
   ],
   "source": [
    "#--------------------------------------------------------------------\n",
    "# Initialize the InSAR data\n",
    "\n",
    "# use data covariance from downsampling\n",
    "#---------------------------------------------------------------\n",
    "# Create insar objectS\n",
    "sarAd1 = insar('A2D166_ifg', utmzone=utmzone, lon0=lon0, lat0=lat0)\n",
    "sarAd1.read_from_varres(varresA2, cov=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acknowledged-document",
   "metadata": {},
   "source": [
    "We add the dataset `sarAd1` to a list of InSAR datasets. In most cases, there will be more than one dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fabulous-attribute",
   "metadata": {},
   "outputs": [],
   "source": [
    "insardata = [sarAd1]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "legislative-hospital",
   "metadata": {},
   "source": [
    "In this example, we don't have any GPS (GNSS) data, so we set that to an empty list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "resistant-dakota",
   "metadata": {},
   "outputs": [],
   "source": [
    "gpsdata = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "marine-shame",
   "metadata": {},
   "source": [
    "We combine the InSAR and GPS data into a list of all geodetic datasets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "collaborative-jacksonville",
   "metadata": {},
   "outputs": [],
   "source": [
    "geodata = insardata + gpsdata "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prospective-arkansas",
   "metadata": {},
   "source": [
    "The new version of CSI has a `transformation` class for dealing with data transformation nuisance parameters. We need to setup a list of the transformations for each geodetic dataset. In this case, we will only use a constant (order 1) for the InSAR dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "military-tobago",
   "metadata": {},
   "outputs": [],
   "source": [
    "transformations = [1]   # estimate constant offset for InSAR"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eligible-necessity",
   "metadata": {},
   "source": [
    "Now we instantiate the `transformation` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "excellent-birth",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "---------------------------------\n",
      "Initializing transformation Orbits and reference frame\n"
     ]
    }
   ],
   "source": [
    "#---------------------------------\n",
    "# Create transformation object\n",
    "\n",
    "trans = transformation('Orbits and reference frame', lon0=lon0, lat0=lat0, utmzone=utmzone, verbose=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "computational-hamburg",
   "metadata": {},
   "source": [
    "For this example, we will create the fault object from the same simplified version of the main fault rupture that was used for the initial downsampling, represented by three points in `Approx-main-rupture.txt` as the surface trace of the fault, and approximate it as a vertical fault.\n",
    "\n",
    "The `verticalfault` class is a class inherited from `RectangularPatches` class to generate a model vertical fault as a set of rectangular patches. The `RectangularPatches` class is in turn a subclass of the `Faults` class. The first parameter is the fault name. The local coordinate system for the CSI calculations is specified by the UTM zone and center point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "whole-sitting",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "---------------------------------\n",
      "Initializing fault Ridgecrest_main\n"
     ]
    }
   ],
   "source": [
    "faultTrace = 'Approx-main-rupture.txt'\n",
    "fault = verticalfault('Ridgecrest_main', utmzone=utmzone, lon0=lon0, lat0=lat0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "strategic-sensitivity",
   "metadata": {},
   "source": [
    "We load the surface trace points from the file into the trace of the fault object with the `file2trace` method and then interpolate between the points with the `discretize` method to densify the trace every 2 km. The interpolation method works better if we tell it which direction 'x' or 'y' to work along as the `xaxis`. In this case, the main Ridgecrest rupture is more north-south."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "certified-quantum",
   "metadata": {},
   "outputs": [],
   "source": [
    "# load fault trace from file\n",
    "fault.file2trace(faultTrace)\n",
    "# fault mostly north-south\n",
    "fault.discretize(every=2.0,xaxis='y')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bridal-globe",
   "metadata": {},
   "source": [
    "Now we use `setDepth` method to set the depth to which the fault will extend (first parameter), the depth of the top, and the number of patches down-dip. For this rough fault, we use 2 km patch size.\n",
    "\n",
    "Then we use the `build_patches` method to generate all the rectangular patches. We run the `initializeslip` method to set the initial slip values on all patches to zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "stylish-circulation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Build patches for fault Ridgecrest_main between depths: 0.0, 20.0\n"
     ]
    }
   ],
   "source": [
    "# Set max depth of 20 km and number of fault patches down-dip as 10 (2 km patches)\n",
    "fault.setDepth(20.0, top=0.0, num=10)\n",
    "# Build the patches\n",
    "fault.build_patches()\n",
    "# Initialize the slip vector to zero\n",
    "fault.initializeslip()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "swedish-rapid",
   "metadata": {},
   "source": [
    "The patches are kept in the `patch` array of the fault, so we can see the total number of patches in the model fault by checking its length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "minimal-withdrawal",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "230"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numPatches = len(fault.patch)\n",
    "numPatches"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "original-algorithm",
   "metadata": {},
   "source": [
    "Now we define the area we will be using for the analysis and for the plotting. These are the same for now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "determined-redhead",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Box for Ridgecrest area data cropping\n",
    "minlat = 35.3\n",
    "maxlat = 36.2\n",
    "minlon = -118.1+360.\n",
    "maxlon = -117.1+360.\n",
    "\n",
    "# Box for plotting (same for now)\n",
    "plotMinLat = minlat\n",
    "plotMaxLat = maxlat\n",
    "plotMinLon = minlon\n",
    "plotMaxLon = maxlon"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "outdoor-argentina",
   "metadata": {},
   "source": [
    "We can use the `geodeticplot` class to make map and 3D plots of the fault. We need to tell it the plotting area when we create the plotting object. The `faulttrace` method loads the trace into the plots, and the `faultpatches` method loads the fault patches.\n",
    "\n",
    "Then we can plot the fault trace in the map view with the `show` method. The `showFig` parameter says to only plot the map view and `fitOnBox` parameter tells it to plot the area of the box we specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "periodic-settle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# View the fault trace\n",
    "gp = geoplt(plotMinLon, plotMinLat, plotMaxLon, plotMaxLat, figure=1)\n",
    "gp.faulttrace(fault, color='r')\n",
    "gp.faultpatches(fault, slip='total')\n",
    "gp.show(mapaxis='equal', showFig=['map'], fitOnBox=True) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "absolute-chance",
   "metadata": {},
   "source": [
    "We can write out the fault patches as a set of polygons in the GMT vector file format for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "german-addition",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Writing geometry to file fault_rect.gmt\n"
     ]
    }
   ],
   "source": [
    "# Write to file for plotting in GMT\n",
    "faultGeoOut = 'fault_rect.gmt'\n",
    "fault.writePatches2File(faultGeoOut)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "freelance-scratch",
   "metadata": {},
   "source": [
    "The slip inversiom and most other CSI classes are designed to work with a set of faults contained in a Python list object, so we need to make a list out of our one fault. In other situations, we could use multiple faults in the list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "functioning-sleeve",
   "metadata": {},
   "outputs": [],
   "source": [
    "faults = [fault]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "common-ukraine",
   "metadata": {},
   "source": [
    "Now we can calculate the Green's functions (GFs) between slip on each patch of each fault and each of the datasets, using the `buildGFs` method of the `Fault` class. We use a outer loop to calculate these in case there is more than one fault and an inner loop over each dataset. For InSAR data, we want to use `vertical=True`. For some GPS datasets we only have horizontal displacements, so we could set that to False. We also specify the slip direction we want to allow. For this example, we will only use strike-slip direction slip so `slipDir='s'`. The default for Green's functions is to use the Okada (1992) equations for homogeneous elastic half-space. The whole set of Green's functions is the matrix `G` in the classic `Gm=d` equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "formed-command",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Greens functions computation method: Okada\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "Building Green's functions for the data set \n",
      "A2D166_ifg of type insar in a homogeneous half-space\n",
      " Patch: 230 / 230  \n"
     ]
    }
   ],
   "source": [
    "#---------------------------------\n",
    "# Set up GFs\n",
    "slipDir = 's'\n",
    "for fault in faults:\n",
    "    for data in geodata:\n",
    "        fault.buildGFs(data, vertical=True, slipdir=slipDir)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "acoustic-paragraph",
   "metadata": {},
   "source": [
    "We also need to generate the transformation Green's functions for all the geodetic datasets with the `buildGFs` method of that class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "banned-blink",
   "metadata": {},
   "outputs": [],
   "source": [
    " trans.buildGFs(geodata, transformations)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "little-detection",
   "metadata": {},
   "source": [
    "Now we need to do what CSI calls the assemble steps for the Green's functions matrix `G`, data `d`, and data covariance `Cd` on each fault and on the transformation object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "antique-presence",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "---------------------------------\n",
      "Assembling G for fault Ridgecrest_main\n",
      "Dealing with A2D166_ifg of type insar\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "Assembling d vector\n",
      "Dealing with data A2D166_ifg\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "Assembling G for transformation Orbits and reference frame\n",
      "---------------------------------\n",
      "---------------------------------\n",
      "Assembling d vector\n",
      "Dealing with data A2D166_ifg\n"
     ]
    }
   ],
   "source": [
    "#---------------------------------\n",
    "# Assemble the Green's functions, data, and data covariance\n",
    "\n",
    "for fault in faults:\n",
    "    fault.assembleGFs(geodata, slipdir=slipDir)\n",
    "    fault.assembled(geodata)\n",
    "    fault.assembleCd(geodata)\n",
    "trans.assembleGFs(geodata)\n",
    "trans.assembled(geodata)\n",
    "trans.assembleCd(geodata)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "industrial-terrace",
   "metadata": {},
   "source": [
    "The next step is to build the model covariance matrix `Cm`. For the least squares slip inversions, the `Cm` is what applies a regularization or smoothing on the slip. We will use an exponential function of distance for the regularization from the Radiguet, et al. (2011) paper, which uses two parameters. As we are only allowing strike-slip motion, we only need `Cm` paramters for the strike-slip component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "indie-blast",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cm_sigmaS=5.    # something like the expected sigma in meters--\"strike\" stronger smoothing\n",
    "Cm_lamS=5.       # smoothing distance in km, will be normalized by lam0 calculated from patch spacing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mysterious-billy",
   "metadata": {},
   "source": [
    "We build up the `Cm` for each fault and for the transformation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "artificial-exception",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "---------------------------------\n",
      "Assembling the Cm matrix \n",
      "Sigma = [5.0]\n",
      "Lambda = [5.0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#---------------------------------------------------------------\n",
    "# Build the model covariance matrix\n",
    "\n",
    "# This command allows to build a model covariance matrix including \n",
    "# a distance-based smoothing. The function is an exponential.\n",
    "# The first parameter is the amplitude of the correlation and the second\n",
    "# is the wavelength.\n",
    "# One parameter per Slip direction\n",
    "\n",
    "for fault in faults:\n",
    "    fault.buildCmSlipDirs([Cm_sigmaS], [Cm_lamS])\n",
    "trans.buildCm(1.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "improved-workstation",
   "metadata": {},
   "source": [
    "The least-squares slip inversion solver is implemented by the `multifaultsolve` class. We instantiate an object from that class with the list of faults and the transformation. The transformation is not a fault, so there is a warning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "threatened-discovery",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "---------------------------------\n",
      "Initializing solver object\n",
      "Not a fault detected\n"
     ]
    }
   ],
   "source": [
    "#--------------------------------------------------------------------\n",
    "# Create a solver and solve\n",
    "\n",
    "slv = multifaultsolve('Ridgecrest', faults+[trans])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "portable-anthropology",
   "metadata": {},
   "source": [
    "Now we can assemble all the Green's functions in the solver and the model covariance matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "conventional-invitation",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data: 465\n",
      "Number of parameters: 231\n",
      "-----------------\n",
      "Fault Name                    ||Strike Slip ||Dip Slip    ||Tensile     ||Coupling    ||Extra Parms \n",
      "Ridgecrest_main               ||   0 -  230 ||None        ||None        ||None        ||None        \n",
      "-----------------\n",
      "Fault Name                    ||Strike Slip ||Dip Slip    ||Tensile     ||Coupling    ||Extra Parms \n",
      "Orbits and reference frame    ||None        ||None        ||None        ||None        || 230 -  231 \n"
     ]
    }
   ],
   "source": [
    "slv.assembleGFs()\n",
    "slv.assembleCm()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "iraqi-bridges",
   "metadata": {},
   "source": [
    "The solver reports the total number of data samples, and the total parameters in the model. We are only solving for strike-slip, so the number of slip parameters is the same as the number of patches. There is one additional transformation parameter in this simple case.\n",
    "\n",
    "Now we are finally ready to run the slip inversion. The `multifaultsolve` class has a number of inversion methods. We will use a basic method `GeneralizedLeastSquareSoln` that has this description:\n",
    "\n",
    "    def GeneralizedLeastSquareSoln(self, mprior=None, rcond=None, useCm=True):\n",
    "        '''\n",
    "        Solves the generalized least-square problem using the following formula (Tarantolla, 2005,         Inverse Problem Theory, SIAM):\n",
    "\n",
    "            :math:`\\\\textbf{m}_{post} = \\\\textbf{m}_{prior} + (\\\\textbf{G}^t \\\\textbf{C}_d^{-1} \\\\textbf{G} + \\\\textbf{C}_m^{-1})^{-1} \\\\textbf{G}^t \\\\textbf{C}_d^{-1} (\\\\textbf{d} - \\\\textbf{Gm}_{prior})`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "successful-paraguay",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "---------------------------------\n",
      "---------------------------------\n",
      "Computing the Generalized Inverse\n",
      "Computing the inverse of the model covariance\n",
      "Computing the inverse of the data covariance\n",
      "Computing m_post\n",
      "Compute cost function\n",
      "6.22817634394949\n",
      "Magnitude is\n",
      "7.222968927408389\n"
     ]
    }
   ],
   "source": [
    "# Do least squares solution\n",
    "slv.GeneralizedLeastSquareSoln()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "instant-detection",
   "metadata": {},
   "source": [
    "Now we distribute the solution model `m` back onto the original set of faults that we provided to the solver object with the `distributem` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "speaking-partition",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Distribute the slip values to the patches\n",
    "slv.distributem()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "falling-penetration",
   "metadata": {},
   "source": [
    "Now we can plot the results with the `geodeticplot` class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "western-thriller",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 2160x2160 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "if plotSlip:\n",
    "# now plot fault slip components\n",
    "    slipMode=slipDir  # we have only one slip direction in this example\n",
    "    gp = geoplt(figure=1,latmin=plotMinLat,lonmin=plotMinLon,latmax=plotMaxLat,lonmax=plotMaxLon)\n",
    "    gp.drawCoastlines(parallels=0.2, meridians=0.2, drawOnFault=True, resolution='fine')\n",
    "    gp.faulttrace(fault, color='r',add=False)\n",
    "    gp.faultpatches(fault, norm=[-2.0,2.0], colorbar=True, slip=slipMode, plot_on_2d=False, revmap=True)\n",
    "    gp.setzaxis(40.0, [0, 10, 20, 30, 40])\n",
    "#\n",
    "    gp.savefig(slipMode+'View', dpi=400,saveFig=['fault'])\n",
    "    gp.show(showFig=['fault'], fitOnBox=False)\n",
    "    gp.clf()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "incorporated-sapphire",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dietary-constitutional",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
